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<p>In TDD-OFDM systems, key generation relies on channel reciprocity, which assumes that the channel characteristics of the transmit and receive channels are identical. However, practical conditions such as hardware asymmetry, environmental noise, and multipath effects often disrupt this reciprocity, negatively impacting the accuracy and consistency of key generation. To address these issues, this paper proposes a novel key generation scheme based on Self-Organizing Maps (SOM) and K-Means quantization algorithm for TDD-OFDM system under unstable channel. The proposed approach uses Channel Reciprocity Learning Network (CRLN) for feature mapping to enhance the similarity of channel characteristics between the base station (Alice) and the legitimate user (Bob), effectively reducing the key disagreement rate. The physical layer key is derived from two sets of similar channel features, and the initial key is generated using the SOM-KMeans clustering algorithm combined with Gray coding. Experimental results demonstrate that this proposed method significantly lowers the key disagreement rate in unstable channels and achieves a superior key generation rate at high Signal-to-Noise Ratios (SNR) compared to other quantization techniques. Furthermore, the initial keys produced successfully pass the NIST randomness tests.</p>

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Physical layer key generation based on SOM and K-Means clustering quantization for TDD-OFDM systems over unstable channels

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月24日

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  • Weijie Tan,
  • Qiangqiang Gao,
  • Zhenling Li,
  • Zhiquan Liu,
  • Chunguo Li,
  • Gang Xu

摘要

In TDD-OFDM systems, key generation relies on channel reciprocity, which assumes that the channel characteristics of the transmit and receive channels are identical. However, practical conditions such as hardware asymmetry, environmental noise, and multipath effects often disrupt this reciprocity, negatively impacting the accuracy and consistency of key generation. To address these issues, this paper proposes a novel key generation scheme based on Self-Organizing Maps (SOM) and K-Means quantization algorithm for TDD-OFDM system under unstable channel. The proposed approach uses Channel Reciprocity Learning Network (CRLN) for feature mapping to enhance the similarity of channel characteristics between the base station (Alice) and the legitimate user (Bob), effectively reducing the key disagreement rate. The physical layer key is derived from two sets of similar channel features, and the initial key is generated using the SOM-KMeans clustering algorithm combined with Gray coding. Experimental results demonstrate that this proposed method significantly lowers the key disagreement rate in unstable channels and achieves a superior key generation rate at high Signal-to-Noise Ratios (SNR) compared to other quantization techniques. Furthermore, the initial keys produced successfully pass the NIST randomness tests.